In this paper, a supervised technique for training radial basis function (RBF) neural network classifiers is proposed. Such a technique, unlike traditional ones, considers the class memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The result is twofold: a significant reduction in the overall classifica...

Human Activity Recognition adalah teknologi yang memperkenalkan gerakan tubuh manusia menggunakan accelerometer, giroskop, global positioning system, dan kamera. Awal munculnya metode support vector machine digunakan untuk mengklasifikasi 2 kelas, sehingga diperlukan pengembangan mengatasi permasalahan multikelas banyaknya dataset berskala besar mengakibatkan kinerja menjadi tidak optimal. Tuju...

Flow number of asphalt–aggregate mixtures as an explanatory factor has been proposed in order to assess the rutting potential of asphalt mixtures. This study proposes a multiple–kernel based support vector machine (MK–SVM) approach for modeling of flow number of asphalt mixtures. The MK–SVM approach consists of weighted least squares–support vector machine (WLS–SVM) integrating two kernel funct...

This paper investigates the ability of several models of Support Vector Machines (SVMs) with alternate kernel functions to predict the probability of occurrence of Essential Hypertension (HT) in a mixed patient population. To do this a SVM was trained with 13 inputs (symptoms) from the medical dataset. Different kernel functions, such as Linear, Quadratic, Polyorder (order three), Multi Layer P...

Statistical machine learning plays an important role in modern statistics and computer science. One main goal of statistical machine learning is to provide universally consistent algorithms, i.e., the estimator converges in probability or in some stronger sense to the Bayes risk or to the Bayes decision function. Kernel methods based on minimizing the regularized risk over a reproducing kernel ...

A new learning algorithm called extreme learning machine (ELM) has recently been proposed for single-hidden layer feedforward neural networks (SLFNs) with additive neurons to easily achieve good generalization performance at extremely fast learning speed. ELM randomly chooses the input weights and analytically determines the output weights of SLFNs. It is proved in theory that ELM can be extend...

The estimation of crop chlorophyll content could provide technical support for precision agriculture. Canopy spectral reflectance was simulated for different chlorophyll levels using radiative transfer models. Then with multiperiod measured hyperspectral data and corresponding chlorophyll content, after extracting six wavelet energy coefficients from the responded bands, an evaluation of soybea...

This paper presents parallelization strategies for the Radial Basis Function-Finite Difference (RBFFD) method. As a generalized finite differencing scheme, the RBF-FD method functions without the need for underlying meshes to structure nodes. It offers high-order accuracy approximation but scales as O(N) per time step, with N being with the total number of nodes. To our knowledge, this is the f...

Radial Basis Function (RBF) interpolation is a common approach to scattered data interpolation. Gaussian Process regression is also a common approach to estimating statistical data. Both techniques play a central role, for example, in statistical or machine learning, and recently they have been increasingly applied in other fields such as computer graphics. In this survey we describe the formul...

In this paper we develop a discrete Hierarchical Basis (HB) to efficiently solve the Radial Basis Function (RBF) interpolation problem with variable polynomial order. The HB forms an orthogonal set and is adapted to the kernel seed function and the placement of the interpolation nodes. Moreover, this basis is orthogonal to a set of polynomials up to a given order defined on the interpolating no...